- Natick MA, US Rama P. Kokku - Natick MA, US Jayaprabha Shankar - Natick MA, US James L. Brock - Kingston NH, US Chun-Yu Shei - Allston MA, US Vijaya Raghavan - Brookline MA, US Yaohung Tsai - Knoxville TN, US
Systems and methods may automatically generate code for deep learning networks. The systems methods may provide a code generation framework for generating target specific code. The code generation framework may include one or more predefined class hierarchies for constructing objects of the generated code. The objects of the class hierarchies may provide an interface to predefined libraries of deep learning functions optimized for use on a target platform. The systems and methods may perform one or more optimizations on the code being generated.
Verification Of Computer-Executable Code Generated From A Model
- Natick MA, US Xiaocang Lin - Sherborn MA, US Jun Yan - Westborough MA, US Peter S. Szpak - Newton MA, US Appa rao Nirakh - Framingham MA, US Jayaprabha Shankar - Brighton MA, US
International Classification:
G06F 9/44
US Classification:
717104
Abstract:
In an embodiment, a model is sliced into a plurality of slices. A slice in the plurality of slices is selected. A portion of code, that corresponds to the selected slice, is identified from code generated from the model. The identified code is verified to be equivalent to the selected slice. Equivalence may include equivalent functionality, equivalent data types, equivalent performance, and/or other forms of equivalence between the selected slice and the identified generated code.